Related Experiment Videos
Identification and quantification of disease-related gene clusters
Gábor Firneisz1, Idit Zehavi, Csaba Vermes
1Section of Biochemistry and Molecular Biology, Departments of Biochemistry, Orthopedic Surgery and Internal Medicine, Rush University at Rush-Presbyterian-St Luke's Medical Center Chicago, IL 60612, USA.
Bioinformatics (Oxford, England)
|September 27, 2003
Summary
Researchers found that genes associated with rheumatoid arthritis tend to cluster together in the mouse genome. This discovery using DNA microarrays may help identify new gene clusters for complex autoimmune diseases.
Area of Science:
- Genomics
- Immunology
- Bioinformatics
Background:
- Advancements in DNA microarray technology and genome sequencing enable new investigations into complex genetic diseases.
- Studying the spatial distribution of disease-related genes within the genome is now feasible.
- This study represents the first systematic search for gene clustering in polygenic autoimmune diseases.
Purpose of the Study:
- To systematically search for clustering of genes associated with a polygenic autoimmune disease.
- To identify spatial patterns of gene expression in disease models.
- To develop methods for discovering functionally related gene clusters.
Main Methods:
- Utilized cDNA microarray chip experiments in two mouse models of rheumatoid arthritis.
- Identified approximately 200 genes based on expression in inflamed joints.
- Mapped identified genes to the genome and computed spatial autocorrelation.
- Applied a friends-of-friends algorithm to identify significant gene clusters.
Main Results:
- Identified approximately 200 disease-associated genes in mouse models of rheumatoid arthritis.
- Found that these genes exhibit a tendency to cluster over scales of a few megabase pairs.
- Successfully identified significant gene clusters using a friends-of-friends algorithm.
Conclusions:
- Genes associated with rheumatoid arthritis demonstrate spatial clustering in the genome.
- The employed methodology can aid in discovering functionally related gene clusters in mammalian genomes.
- This approach offers new insights into the genomic organization of complex diseases.